Training on Data Management and Statistical Analysis using SPSS

Training Course on Data Management and Statistical Analysis using SPSS

Venue: Kigali, Rwanda

Dates

Registration link

18 - 22, Mar 2024

https://bit.ly/491nWug

13 - 17, May 2024

https://bit.ly/491nWug

15 - 19, Jul 2024

https://bit.ly/491nWug

09 - 13, Sep 2024

https://bit.ly/491nWug

25 - 29, Nov 2024

https://bit.ly/491nWug

About the Course

Large and complex dataset of socio-economic and business context often demand statistical analysis using SPSS. IBM SPSS Statistics is a powerful statistical software that make it easy to manage data, conduct accurate analysis and arrive at informed decisions in shorter timing. It delivers a robust set of features that assist researchers in extracting actionable insights from data. The software is more popular in social sciences. Upon completion of this training course on “Data Management and Statistical Analysis using SPSS”, participants will develop competence in quantitative techniques in data management, statistical data analysis, visualization, interpretation and reporting of results.

Target Participants

This course is suitable for researchers, academicians, students, and all who are interested in enhancing their quantitative data analysis skills in their institutions (private sector, education institutions, research institutions, NGOs, etc).

What you will learn

By the end of the course the learner should be able to:

• Clean their data for use in subsequent statistical analysis.
• Identify and fix errors in datasets.
• Manipulate data to make it fit for statistical analysis.
• More quickly understand large and complex data sets with advanced statistical procedures that help ensure high accuracy and quality decision-making.
• Gain high level skills on statistical results interpretation and report writing.

Course duration

5 days

Course Outline

Module 1: Statistical Concepts

• Introduction
• Types of data
• Data structures and types of variables
• Overview of SPSS
• Working with the SPSS software (file management, editing functions, viewing options, etc)
• Output management
• Basics programming of SPSS

Module 2: Data Entry/Management

• Entering data in SPSS
• Defining and labeling variables
• Validation and sorting variables
• Transforming, recoding and computing variables
• Restructuring data
• Replacing missing values
• Merging files and restructuring
• Splitting files, selecting cases and weighing cases
• Syntax and output

Graphics using SPSS

• Introduction to graphs in SPSS
• Graph commands in SPSS
• Different types of Graphs in SPSS

Module 3: Statistical Inference and Descriptive Statistics

Introduction

• Types of statistical tests (Association/relationships, differences, causality, etc)
• Hypothesis testing

Basic Statistics using SPSS

• Descriptive statistics for numeric variables
• Frequency tables
• Distribution and relationship of variables
• Cross tabulations of categorical variables
• Stub and banner tables

Correlation

• Correlation of bivariate data
• Subgroup correlations
• Scatterplots of data by subgroups
• Overlay scatterplots

Comparing Means

• One sample t-tests
• Paired sample t-tests
• Independent samples t-tests
• Comparing means using One-Way ANOVA
• MANOVA: multivariate analysis of variance
• Repeated measures ANOVA

Tests of Associations

• Goodness of Fit Chi Square – All categories equal
• Goodness of Fit Chi Square – Categories unequal
• Chi Square for contingency tables
• Pearson correlation
• Spearman correlation

Nonparametric Statistics

• Mann-Whitney test
• Wilcoxon’s Matched Pairs Signed-Ranks Test
• Kruskal-Wallis One-Way ANOVA
• Friedman’s Rank Test for k Related Samples

Comparing Means Using Factorial ANOVA

• Factorial ANOVA Using GLM Univariate
• Simple Effects

Module 4: Advanced Analysis and Modeling using SPSS

Predictive Models using SPSS

• Linear Regression
• Multiple Regression
• Logistic Regression
• Ordinal Regression

Longitudinal Analysis using SPSS

• Features of Longitudinal Data
• Exploring Longitudinal data
• Longitudinal analysis for continuous outcomes

Time Series and Forecasting using SPSS

• The basics of forecasting
• Smoothing of time series data
• Regression with time series data
• ARIMA models

Module 5: Revision and Other Topics

Revision

• Data analysis using SPSS project
• Guided revision

Other topics

• Cluster Analysis
• Factor analysis
• Survival Analysis (Kaplan-Meier)